Stud.IP Uni Oldenburg
University of Oldenburg
04.10.2022 12:33:40
inf535 - Computational Intelligence I (Complete module description)
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Module label Computational Intelligence I
Modulkürzel inf535
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Computing Science
Verwendbarkeit des Moduls
  • Master Applied Economics and Data Science (Master) > Data Science
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik
  • Master's Programme Computing Science (Master) > Angewandte Informatik
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction
  • Master's Programme Environmental Modelling (Master) > Mastermodule
Zuständige Personen
Kramer, Oliver (Module responsibility)
Lehrenden, Die im Modul (Prüfungsberechtigt)
Prerequisites
Skills to be acquired in this module
Professional competence:
The students:
  • recognise optimisation problems
  • implement simple algorithms of heuristic optimisation
  • critically discuss solutions and selection of methods
  • deepen previous knowledge of analysis and linear algebra


Methodological competence
The students:
  • deepen programming skills
  • apply modelling skills
  • learn about the relation between problem class and method selection


Social competence
The students:
  • cooperatively implement content introduced in lecture
  • evaluate own solutions and compare them with those of their peers


Self-competence
The students:
  • evaluate own skills with reference to peers
  • realize personal limitations
  • adapt own problem solving approaches with reference to required method competences
Module contents
Computational Intelligence comprises intelligent and adaptive methods for optimisation and learning. The module "Computational Intelligence I" concentrates on methods for evolutionary optimisation and heuristic approaches. The exercises introduce and deepen practical aspects of the implementation and algorithmic design, also taking into account application aspects.

Overview of Content:
  • foundations of optimisation
  • genetic algorithms and evolution strategies
  • parameter control and self-adaptation
  • runtime analysis
  • swarm algorithms
  • constrained optimisation
  • multi-objective optimisation
  • meta-modeling
Literaturempfehlungen
  • EIBEN, A. E.; SMITH, J. E.: Introduction to Evolutionary Computing. Springer, 2003.
  • KENNEDY, J.; EBERHART, R.C.; YUHUI, S.: Swarm Intelligence. Morgan Kaufmann, 2001.
  • KRAMER, O.: Computational Intelligence. Springer, 2009.
  • RUTKOWSKI, L.: Computational Intelligence - Methods and Techniques. Springer, 2008.
  • ROJAS, R.: Theorie der neuronalen Netze: Eine systematische Einführung. Springer, 1993.
Links
Languages of instruction English, German
Duration (semesters) 1 Semester
Module frequency jährlich
Module capacity unlimited
Modullevel / module level AS (Akzentsetzung / Accentuation)
Modulart / typ of module je nach Studiengang Pflicht oder Wahlpflicht
Lehr-/Lernform / Teaching/Learning method
Vorkenntnisse / Previous knowledge - Grundlagen der Statistik
Form of instruction Comment SWS Frequency Workload of compulsory attendance
Lecture 2 WiSe 28
Exercises 2 WiSe 28
Präsenzzeit Modul insgesamt 56 h
Examination Prüfungszeiten Type of examination
Final exam of module
At the end of the lecture period
Written or oral exam